Recently, intelligent reflecting surface (IRS) has been proposed as a low-cost technology to achieve spectral and energy efficient wireless networks [1]}, [2]}. Specifically, by smartly coordinating the reflection phase shifts of a large number of passive elements at IRS, wireless propagation channels between transceivers can be reconfigured in real-time to achieve different design objectives, such as signal focusing and interference suppression. In particular, the fundamental performance limit of IRS was firstly derived in [2]} which proves that IRS is able to provide an asymptotic squared power gain in terms of the user receive power via passive beamforming. Such a promising power scaling law of IRS has then motivated an intensive research interest in investigating joint active and passive beamforming for various IRS-aided systems (see [4]}, [5]}, [6]}, [1]}, [2]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]} and the references therein). While the above works focused on applying IRS to assist wireless information transmission (WIT), it is also practically appealing to make use of the high passive beamforming gain of IRS for improving the WPT efficiency [1]}. Specifically, leveraging intelligent reflections over large aperture IRSs can effectively compensate the severe distance-based signal attenuation and helps establish local energy harvesting/charging zones in their vicinity, thus leading to a largely extended service coverage of WPT. This is of crucial importance for widening the practical use-cases of WPT in multifarious application scenarios and unlocking its full potential in achieving the promising battery-free IoT networks in the future.
